The Internet of Vehicles (IoV) faces critical challenges in balancing real-time data processing, privacy preservation, and secure data sharing amid growing intelligent transportation demands. While mobile edge computing (MEC) reduces latency by offloading tasks to MEC servers, efficient encrypted search and dynamic access control remain unresolved. Attribute-based keyword search (ABKS) enables privacy-preserving queries on encrypted data but exhibits critical limitations such as lack of revocable access for dynamic user privileges, exposed access policy that risk sensitive attribute leakage, and data integrity verification. Moreover, existing ABKS schemes further suffer from centralized key management in attribute-based encryption (ABE), introducing single points of failure and key escrow issues. To address these issues, we propose BC-RMABSE, a blockchain-enabled ABKS scheme. Our scheme leverages the vector space model to enable ranked multi-keyword searches, returning top-k relevant results for improved efficiency. Policy-hiding mechanisms and attribute revocation ensure flexible fine-grained access control while safeguarding sensitive attributes. A decentralized key distribution strategy using Pedersen’s (k, n) secret sharing protocol eliminates reliance on central authority, mitigating security risks. Blockchain technology enforces data integrity through tamper-proof consensus and resolves the "service-payment" imbalance via smart contracts, ensuring transactional fairness between users and untrusted service providers. Experimental analysis indicates that our scheme performs well in terms of both security and search efficiency.

Smart healthcare provides more convenient services to patients and medical institutions. However, it also suffers from medical data privacy leakage and broad query results. Although many dynamic symmetric searchable encryption (DSSE) schemes solve these problems, they still suffer from forward and backward security, the absence of non-interactive queries, and large computational and communication overhead for conjunctive queries. To address these issues, we propose a conjunctive forward-backward secure dynamic symmetric searchable encryption scheme for smart healthcare called CFB-DSSE. First, we utilize inner product predicate encryption technology to achieve conjunctive queries while preserving user privacy. Second, we employ tailored proxy re-encryption technology to ensure forward and backward security, thereby enhancing smart healthcare system security. Third, we design a double-layer encrypted bitmap index structure to replace the traditional homomorphic encryption technology to encrypt the bitmap index, reducing computational and communication overhead and enabling non-interactive queries. Through experiments and analysis, CFB-DSSE scheme demonstrates considerable advancements in protecting medical data privacy and improving query efficiency. CFB-DSSE can save at least 100ms of search time compared to other related schemes.

Abstract The ever-growing multi-modal images pose great challenges to local image storage and retrieval systems. Cloud computing provides a solution to large-scale image data storage but suffers from privacy issues and lacks the support for multi-modal image retrieval. To address these, a searchable encryption-empowered privacy-preserving multi-modal image retrieval method is proposed. First, we design a hybrid image retrieval framework that fuses visual features and textual features at a decision level and further supports similar image retrieval and multi-keyword image retrieval. Second, we construct a new hybrid inverted index structure to distinguish high-frequency terms from low-frequency terms and index them through hierarchical index trees and data blocks, respectively, which greatly improves query efficiency. Third, we design a prime encoding-based multi-keyword query method that converts mapping operations in bloom filters into inner product calculations, and further implements secure multi-keyword image query. Experiments against the Baseline schemes are conducted to verify the performance of the scheme in terms of high efficiency.

Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utility-security dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be extracted for similar image recovery. Security analysis demonstrates that image privacy and query privacy could be well protected. Moreover, the proposed work achieves more efficient performance for similarity search and similar image recovery compared with some recent works and realizes a reasonable retrieval accuracy on encrypted images for similarity search.

With the development and innovation of new techniques for 5G, 5G networks can provide extremely large capacity, robust integrity, high bandwidth, and low latency for multimedia image sharing and storage. However, it will surely exacerbate the privacy problems intrinsic to image transformation. Due to the high security and reliability requirements for storing and sharing sensitive images in the 5G network environment, verifiable steganography-based secret image sharing (SIS) is attracting increasing attention. The verifiable capability is necessary to ensure the correct image reconstruction. From the literature, efficient cheating verification, lossless reconstruction, low reconstruct complexity, and high-quality stego images without pixel expansion are summarized as the primary goals of proposing an effective steganography-based SIS scheme. Compared with the traditional underlying techniques for SIS, cellular automata (CA) and matrix projection have more strengths as well as some weaknesses. In this paper, we perform a complimentary of these two techniques to propose a verifiable secret image sharing scheme, where CA is used to enhance the security of the secret image, and matrix projection is used to generate shadows with a smaller size. From the steganography perspective, instead of the traditional least significant bits replacement method, matrix encoding is used in this paper to improve the embedding efficiency and stego image quality. Therefore, we can simultaneously achieve the above goals and achieve proactive and dynamic features based on matrix projection. Such features can make the proposed SIS scheme more applicable to flexible 5G networks. Finally, the security analysis illustrates that our scheme can effectively resist the collusion attack and detect the shadow tampering over the persistent adversary. The analyses for performance and comparative demonstrate that our scheme is a better performer among the recent schemes with the perspective of functionality, visual quality, embedding ratio, and computational efficiency. Therefore, our scheme further strengthens security for the images in 5G networks.